A Data Fusion Approach in Protein Homology Detection

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2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies

June 29-July 05

ISBN: 978-0-7695-3191-5

	ASCII Text	x
	Aydin Can Polatkan, Hasan Ogul, Hayri Sever, "A Data Fusion Approach in Protein Homology Detection," International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, pp. 7-12, 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, 2008.

	BibTex	x
	@article{ 10.1109/BIOTECHNO.2008.23, author = {Aydin Can Polatkan and Hasan Ogul and Hayri Sever}, title = {A Data Fusion Approach in Protein Homology Detection}, journal ={International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies}, volume = {0}, year = {2008}, isbn = {978-0-7695-3191-5}, pages = {7-12}, doi = {http://doi.ieeecomputersociety.org/10.1109/BIOTECHNO.2008.23}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies TI - A Data Fusion Approach in Protein Homology Detection SN - 978-0-7695-3191-5 SP7 EP12 A1 - Aydin Can Polatkan, A1 - Hasan Ogul, A1 - Hayri Sever, PY - 2008 KW - protein homology detection KW - n-peptite compositions KW - support vector machines KW - data fusion VL - 0 JA - International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies ER -

Aydin Can Polatkan

Hasan Ogul

Hayri Sever

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BIOTECHNO.2008.23

The discriminative framework for protein remote homology detection based on support vector machines (SVMs) is reconstructed by the fusion of sequence based features. In this respect, n-peptide compositions are partitioned and fed into separate SVMs. The SVM outputs are evaluated with different techniques and tested to discern their ability for SCOP protein super family classification on a common benchmarking set. It reveals that the fusion approach leads to an improvement in prediction accuracy with a remarkable gain on computer memory usage.

Index Terms:

protein homology detection, n-peptite compositions, support vector machines, data fusion

Citation:

Aydin Can Polatkan, Hasan Ogul, Hayri Sever, "A Data Fusion Approach in Protein Homology Detection," biotechno, pp.7-12, 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, 2008

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